sarwarbeing-ai/Agentic_Design_Patterns
Agentic Design Patterns
This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion.
The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems.
The architectural surface covers autonomous workflow automation, including plan-and-execute workflows, tool-use execution loops, and iterative prompt refinement. It also includes mechanisms for dynamic context windowing to optimize model attention.
The project is implemented as a series of Jupyter Notebooks.
Features
- Autonomous Agent Patterns - Provides architectural templates and structural patterns to increase autonomy and reasoning in AI agents.
- Agent Architectures - Implements structural design patterns specifically for LLM-based autonomous agents.
- Multi-Agent Orchestration Frameworks - Coordinates specialized agents through task delegation and state sharing to solve complex problems.
- AI Agent Architectures - Designs structural patterns that improve the reliability and autonomy of complex task completion.
- Agentic Workflow Frameworks - Provides a framework of structural patterns for coordinating multiple LLM interactions.
- Multi-Agent Orchestrators - Coordinates specialized agents using task delegation and state sharing to solve complex problems.
- Self-Correction Loops - Implements loops where models review and correct their own outputs against specific criteria.
- Reasoning Orchestrators - Implements methods for managing reflection and self-correction cycles within LLM applications.
- Reasoning Workflows - Implements structural patterns for reflection and iterative refinement to enhance model output quality.
- Tool-Execution Loops - Provides a cycle where models select functions and update plans based on real-time tool output.
- Tool-Use Orchestration - Implements the orchestration of interleaving reasoning with external tool execution cycles.
- Agentic Plan-And-Execute Workflows - Implements the plan-and-execute architectural pattern to separate strategic decomposition from tactical execution.
- Autonomous Agent Loops - Provides principles for transitioning from simple loops to goal-oriented planning and execution cycles.
- Autonomous Workflow Automation - Builds self-correcting systems capable of planning, executing, and refining tasks autonomously.
- Context Window Optimizations - Implements mechanisms to filter and inject relevant information to optimize the LLM context window.
- Prompt Iteration Workflows - Provides a recursive process for adjusting prompts based on execution failures to improve output quality.
- LLM Reasoning Workflows - Applies specific design workflows to enhance the logical reasoning and problem-solving capabilities of LLMs.
